Notes: AI/ML Product Management
Why AI/ML Product Management?
Even before I started pursuing a degree in cognitive science, I’ve always been interested in understanding human brain—dubbed by many as “the most complex structure in the universe”— and how they give rise to phenomena like intelligence and consciousness. Looking back, it’s led me down a path that has cultivated a deep fascination for the symbiotic relationship between neuroscience and artificial intelligence (AI), and how advancements in one field often provoke mind-blowing insights into the other, and vice-versa. To match this fascination, AI’s potential impact—and the risk it brings—across industries, societies, and the world has been increasingly discussed & apparent in recent years.
And throughout my career, this has inspired me to keep finding ways to inch ever-closer to working with/in the field of AI and accelerate its positive impact and application in the world. It’s helped guide me landing on my current role now as a product manager for the applied machine learning /computer vision team at Yahoo.
I’m grateful for the exciting opportunity to work with talented researchers and engineers to guide application of AI for improving our products and unlocking new user experiences that wasn’t possible before—and eager to continue learning as much as I can about this space along the way.
But while there’s tons of great product management lore out there or technical resources for machine learning (and even more that explain what it is and why it’s important), I’ve struggled to find resources specifically for product managers that work in AI/ML teams and can speak to the unique product challenges they face:
- Unlike traditional products, AI/ML features & products introduce (1) a dependency on data, (2) unpredictable / unexplainable / unrepeatable outcomes, and (3) the risk of degenerative performance over time due to shifting trends in user behavior.
- In addition to working with (1) software engineers to build out capabilities and (2) designers to make products visually appealing and usable, they also interface and coordinate amongst members of the data landscape, including: (3) ML researchers who focus on prototyping new algorithms, (4) data engineers who robustly scale systems, and (5) data science generalists who also work across these boundaries.
I’m big on the impact that AI will have on the industry, and how it will redefine the product management techniques we use to build AI products and businesses. Over time, this article will summarize my learning & key resources on this emerging discipline.
In between, I plan to continuously capture smaller nuggets of learning in a Medium Series, which will include a combination of (1) resources I encounter (2) discussions with people in the field, and (3) personal work experiences.
Follow Along My Journey!
These are some of my experiences and active projects in this area of interest:
⭐️ My Milestones
These are some things I’ve been working on along the way:
- ⏳️ Currently a PM for Yahoo’s Applied Computer Vision team
- ⏳️️️ Currently tinkering with combined AI & UX prototyping
- ✅ Completed Udacity’s Computer Vision Nanodegree
- ✅ Completed Udacity’s Artificial Intelligence Nanodegree
- ✅ Prototyped wearable gesture recognition as a 20% project
- ✅ Researched machine learning on neuroimaging data in college
📝 My Open Diary
Follow along my Medium Series as I document my learnings:
📚 My Reading List